A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
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Updated
Dec 29, 2024 - Jupyter Notebook
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Harness LLMs with Multi-Agent Programming
AICI: Prompts as (Wasm) Programs
The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM Observability all in one place.
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
[AI Agent Application Development Framework] - 🚀 Build AI agent native application in very few code 💬 Easy to interact with AI agent in code using structure data and chained-calls syntax 🧩 Enhance AI Agent using plugins instead of rebuild a whole new agent
Low code tool to rapidly build and coordinate multi-agent teams
Langtrace 🔍 is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorDBs and more.. Integrate using Typescript, Python. 🚀💻📊
On-Call Assistant for Prometheus Alerts - Get a head start on fixing alerts with AI investigation
Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured output. Works also with models not fine-tuned to JSON output and function calls.
Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework
InternEvo is an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.
FineTune LLMs in few lines of code (Text2Text, Text2Speech, Speech2Text)
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
An ultra-lightweight Agentic AI framework based on the ReAct paradigm, supporting mainstream LLMs and is stronger than Swarm.
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
ICLR 2024 论文和开源项目合集
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